DeepVO reports impressive results on estimating the pose of driving vehicles, even in previously unseen scenarios. In the experiment on the KITTI odometry dataset[1]}, this data-driven solution outperforms conventional representative monocular VO, e.g. VISO2[2]} and ORB-SLAM (without loop closure) [3]}.
Another advantage is that supervised VO naturally produces trajectory with the absolute scale from monocular camera,
while classical VO algorithm is scale-ambiguous using only monocular information.
This is because deep neural network can implicitly learn and maintain the global scale from large collection of images, which can be viewed as learning from past experience to predict current scale metric.
